Discovering long term dependencies in noisy time series data using deep learning
arXiv:2011.07551
Abstract
Time series modelling is essential for solving tasks such as predictive maintenance, quality control and optimisation. Deep learning is widely used for solving such problems. When managing complex manufacturing process with neural networks, engineers need to know why machine learning model made specific decision and what are possible outcomes of following model recommendation. In this paper we develop framework for capturing and explaining temporal dependencies in time series data using deep neural networks and test it on various synthetic and real world datasets.
References in corpus (7)
- An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
- Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
- Exploring Interpretable LSTM Neural Networks over Multi-Variable Data
- MGP-AttTCN: An Interpretable Machine Learning Model for the Prediction of Sepsis
- Interpreting a Recurrent Neural Network's Predictions of ICU Mortality Risk
- Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models
- Discovering Nonlinear Relations with Minimum Predictive Information Regularization